Functional parcellation of mouse visual cortex using statistical techniques reveals response-dependent clustering of cortical processing areas
Fig 3
Analysis of visual cortex responses to different stimuli using supervised GMM classifiers.
A, B) The boundaries obtained by classifying all the pixels using the GMM classifier. Each color represents the visual area identified by the classifier and the black boundary within the cortex corresponds to ground truth retinotopic boundaries. The values within the bracket denote the classification accuracy. In A, the results are compared across different visual stimuli. The title of the plot indicates the visual stimuli shown to the mice. In B, the supervised classifier is verified to be consistent across different mice for natural movie stimuli. C) Results on resting state responses for two mice. D) Pixels selected for training the supervised model are limited to center x% of the radius of the visual area. This x% is mentioned as sample radius in the title of the plots in the first row of D, and the pixel used for training the supervised model is shown as black dots. The corresponding classification boundaries are shown in the second row of D, and the “ACC” values denote the accuracy.